The Template Tool has been designed for use in automatically
locating deformable shape in noisy (generally medical) data sets.
Using it well requires some familiarity with the data
and a little thought as to what is inteneded to be achieved.
Start by determining the best number of points
needed to accurately define the boundary of the object and whether
an inner contour is also needed. Then experiment with the data to
try to determine a reproducable way of positioning boundary points
as accurately as possible. draw up guidelines for features to look
out for and try to define the location of the boundary in a simple and
reproducable manner. If necessary images can be pre-processed by the
Imcalc Tool in order to assist with this process (eg:
enhancing edges etc.).

Once you have
a good idea of what you need to do then proceed with the following steps;

Mark up a series of example profiles which you feel span the space of
possible shape and grey-level variation. The number you will need
will depend upon the degree of variability in the data and cannot
be specified a-priori, but even simple models will generally require tens of
examples.

Construct a list file for the data you wish to include in the
model and train using the PCA model builder. Use a maximum
number of parameters without global covariance at this stage.

Test the performance of the model on unseen datasets varying
the number of parameters used for the shape and grey level variation.
Try to use a minimum of the number of shape parameters which accurately
locates the boundary. Investigate the effects of robust optimisation and
data normalisation to see if there are any particular advatages
to using these options. Always take care that you use the same cost
function options during search that were used during PCA analysis
and never attempt to search with more parameters than were written out
during the analysis process (you can however use fewer).

Test on additional datasets and if the location process repeatedly
fails for a particular image mark it up and add it to the dataset for
retraining.

As the set of example datasets increases the posibility of building
a global shape and grey-level model improves. This should only be done
if the model has repeated difficulty with locating data.
Eventually, (generally quite soon) you should converge on a model which
gives reliable performance for this data set. Accuracies of better
than a pixel are generally achievable at all points around the contour
with some care even in noisy data. Significantly better performance
than this is generally
difficult to achieve due to the restrictions imposed by the eigen model
and algorithms such as edge detection may
be better if there is low noise and strong edge data is available.
These techniques should not be considered
as competetors among the solutions for boundary location.
It is even acceptable to use templates in order to locate edges for more
accurate measurement.

The posibilities for data pre-processing before building the deformable model
are limitless, but an attempt should be made to work with data-sets
which have the properties of uniform random errors (see the Imcalc
Tool) as this is what is most consistent with the statistical
assumptions behind greylevel modelling
and least-squares based location algorithms.

The resulting tracking variables of orientation, scale and principle
eigen-modes of the shape model make a good starting point as a reduced
representation of the data for any subsequent anaysis such as statistical
pattern recognition (eg: classification). However,
these techniques are not expected to form the basis of a generic
object recognition system as there is no robust way of automatically
selecting or building models for arbitrary image data sets.